1. Field of the Invention
The present invention relates to a method and apparatus for extracting a face feature, and more particularly, to a method and apparatus for extracting a face feature, wherein a weight is set according to the order of acquiring images and a face feature is not affected by the number of image data of a candidate so that a face search system has a high recognition rate.
2. Description of the Related Art
A biometric system such as face recognition performs authentication and identification according to a type of service. In authentication, the identity of a user is confirmed using a one-to-one comparison method. In identification, the identity of a most similar person to a predetermined person is confirmed using a one-to-many biometric data comparison for a plurality of persons registered in a database. That is, in authentication, a binary class (or two class) result value is generated as yes or no, whereas in identification, a list of candidates is generated in the order of the decreasing probability as a result value.
Face recognition methods may be divided into methods based on two-dimensional (2D) photographs and methods based on three-dimensional (3D) modeling. The methods based on 3D modeling are advantageous in that a high recognition rate and stable performance can be achieved in various environments, but are disadvantageous in that expensive equipment is required and a large amount of computation for recognition makes it difficult to recognize a face in real time. Meanwhile, although the methods based on 2D photographs are disadvantageous in that recognition performance is influenced a lot by illumination, the direction of a face, and the change in facial expression, they are advantageous in that fast recognition can be accomplished with inexpensive equipment. Accordingly, a method based on 2D photographs is preferred to a method based on 3D modeling for use in a search system.
Face feature extraction is most essential to a 2D face recognition system based on photographs. Since an original face photograph has high-dimensional data, when it is used in the 2D face recognition system as it is, system efficiency is decreased due to a large amount of computation. For this reason, the 2D face recognition system requires feature extraction, in which a face portion suitable for recognition is extracted from an original face image or the original face image is reconstructed into a format suitable for recognition.
For instance, when a black-and-white image having a size of 64×64 pixels is used in a face recognition system, 64×64=4,096-dimensional data is used. Although a 64×64 pixel image does not seem very big in present computer technology, 4,096-dimensional data is high-dimensional data in terms of data analysis and processing with respect to present computation performance. In addition, the original 4,096-dimensional data is not necessary to be used as it is since redundant information or unnecessary information (e.g., noise) exists in the pixels. Consequently, to construct an efficient recognition system, dimension reduction to express 4,096-dimensional image data in a low-dimensional data format or feature extraction is needed.
Cases of low-dimensional data or features that can be generated from a high-dimensional image or data are almost infinite. Accordingly, a predetermined standard is needed in order to generate and select significant low-dimensional data and optimized low-dimensional data is generated based on the predetermined standard. This standard for dimension reduction is referred to as a criterion. Different dimension reduction methods have different criterions. For example, principal component analysis (PCA), which is the most widely used dimension reduction method, has a variance of data as the criterion. In other words, the higher the variance of data is in a low-dimensional space, the better high-dimensional data is expressed in low dimensions. As another example, Fisher linear discriminant (FLD) or Linear discriminant analysis (LDA) uses as the criterion a ratio between a between-class scatter and a within-class scatter. In other words, high-dimensional data is rewritten as low-dimensional data so that the ratio between the two scatters is maximum in the low-dimensional.
Actual dimension reduction is accomplished by projecting high-dimensional data on a base vector, i.e., a projection vector. When high-dimensional data is applied to a criterion of the aforementioned current dimension reduction methods, a projection vector that maximizes or minimizes a value of the criterion is calculated. In order to obtain the projection vector, optimization is used. In many wide-spread dimension reduction methods, the projection vector can be easily obtained by solving an eigenvalue problem or a generalized eigenvalue problem. Accordingly, it can be concluded that a criterion itself defines a dimension reduction method.
The biggest problems of 2D-based face recognition, and particularly of LDA or FLD, are generalization and overfit. Recognition performance is satisfactory with respect to a person whose face image has been used during the generation of a base vector but is not satisfactory with respect to a person whose face image has not been used during the generation of the base vector. In addition, when many face images of different persons are used to generate the base vector, recognition performance is higher than in other cases. Also, since a human face changes little by little over time, when images as recent as possible are used in base vector generation and feature extraction and registration, satisfactory recognition performance can be expected.